可否用超像素池化来改进马尔科夫判别器(PatchGAN),请给出pytorch代码
时间: 2023-09-24 08:03:37 浏览: 84
可以使用超像素池化来改进马尔科夫判别器(PatchGAN),代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class SuperPatchDiscriminator(nn.Module):
def __init__(self, in_channels=3, num_classes=1, num_superpixels=64):
super(SuperPatchDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=4, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
self.conv5 = nn.Conv2d(512, 1 + num_classes, kernel_size=4, stride=1, padding=1)
self.num_superpixels = num_superpixels
def forward(self, x, y=None):
x = F.leaky_relu(self.conv1(x), 0.2, inplace=True)
x = F.leaky_relu(self.conv2(x), 0.2, inplace=True)
x = F.leaky_relu(self.conv3(x), 0.2, inplace=True)
x = F.leaky_relu(self.conv4(x), 0.2, inplace=True)
x = self.conv5(x)
if y is not None:
y = self.superpixel_pooling(y)
x = x + torch.sum(y * x, dim=(2, 3), keepdim=True)
return x
def superpixel_pooling(self, x):
b, c, h, w = x.size()
sp_h, sp_w = h // self.num_superpixels, w // self.num_superpixels
x = x.reshape(b, c, sp_h, self.num_superpixels, sp_w, self.num_superpixels)
x = x.permute(0, 1, 3, 5, 2, 4).contiguous()
x = x.reshape(b, c, self.num_superpixels ** 2, sp_h, sp_w)
x = F.softmax(x, dim=2)
return x
```
这里实现了一个超像素池化的判别器,它在 PatchGAN 的基础上增加了超像素池化层。具体来说,它将输入的真实图像和生成图像分别输入到超像素池化层中,得到每个超像素区域的概率分布,然后将这些概率分布加权求和,并将结果加到 PatchGAN 的输出上。这样做可以使判别器更加关注图像的全局结构,从而提高生成图像的质量。
使用时,可以将输入的真实图像和生成图像分别输入到判别器中,并指定超像素池化层需要的超像素数量。
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